default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDAaron Roth 0001
Aaron Leon Roth
Person information
- affiliation: University of Pennsylvania, Department of Computer and Information Science, Philadelphia, PA, USA
- affiliation: Microsoft Research New England, Cambridge, MA, USA
- affiliation (PhD 2010): Carnegie Mellon University, Department of Computer Science, Pittsburgh, PA, USA
- not to be confused with: Aaron M. Roth
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[c117]Rongting Zhang, Martín Bertrán, Aaron Roth:
Order of Magnitude Speedups for LLM Membership Inference. EMNLP 2024: 4431-4443- [c116]Ira Globus-Harris
, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth:
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. FAccT 2024: 529-545 - [c115]Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth:
Balanced Filtering via Disclosure-Controlled Proxies. FORC 2024: 4:1-4:23 - [c114]Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Oracle Efficient Algorithms for Groupwise Regret. ICLR 2024 - [c113]Gianluca Detommaso, Martin Bertran Lopez, Riccardo Fogliato, Aaron Roth:
Multicalibration for Confidence Scoring in LLMs. ICML 2024 - [c112]Shuai Tang, Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth:
Membership Inference Attacks on Diffusion Models via Quantile Regression. ICML 2024 - [c111]Lujing Zhang, Aaron Roth, Linjun Zhang:
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. ICML 2024 - [c110]Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu:
Improved Differentially Private Regression via Gradient Boosting. SaTML 2024: 33-56 - [c109]Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth:
Oracle Efficient Online Multicalibration and Omniprediction. SODA 2024: 2725-2792 - [e2]Shipra Agrawal, Aaron Roth:
The Thirty Seventh Annual Conference on Learning Theory, June 30 - July 3, 2023, Edmonton, Canada. Proceedings of Machine Learning Research 247, PMLR 2024 [contents] - [i129]Aaron Roth, Mirah Shi:
Forecasting for Swap Regret for All Downstream Agents. CoRR abs/2402.08753 (2024) - [i128]Ira Globus-Harris, Declan Harrison, Michael Kearns, Pietro Perona, Aaron Roth:
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning. CoRR abs/2402.10795 (2024) - [i127]Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth, Mirah Shi:
An Elementary Predictor Obtaining 2√T Distance to Calibration. CoRR abs/2402.11410 (2024) - [i126]Natalie Collina, Varun Gupta, Aaron Roth:
Repeated Contracting with Multiple Non-Myopic Agents: Policy Regret and Limited Liability. CoRR abs/2402.17108 (2024) - [i125]Gianluca Detommaso, Martín Bertrán, Riccardo Fogliato, Aaron Roth:
Multicalibration for Confidence Scoring in LLMs. CoRR abs/2404.04689 (2024) - [i124]Lujing Zhang, Aaron Roth, Linjun Zhang:
Fair Risk Control: A Generalized Framework for Calibrating Multi-group Fairness Risks. CoRR abs/2405.02225 (2024) - [i123]Marcel Hussing, Michael Kearns, Aaron Roth, Sikata Bela Sengupta, Jessica Sorrell:
Oracle-Efficient Reinforcement Learning for Max Value Ensembles. CoRR abs/2405.16739 (2024) - [i122]Ira Globus-Harris, Varun Gupta, Michael Kearns, Aaron Roth:
Model Ensembling for Constrained Optimization. CoRR abs/2405.16752 (2024) - [i121]Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Reconstruction Attacks on Machine Unlearning: Simple Models are Vulnerable. CoRR abs/2405.20272 (2024) - [i120]Buxin Su, Jiayao Zhang, Natalie Collina, Yuling Yan, Didong Li, Kyunghyun Cho, Jianqing Fan, Aaron Roth, Weijie J. Su:
Analysis of the ICML 2023 Ranking Data: Can Authors' Opinions of Their Own Papers Assist Peer Review in Machine Learning? CoRR abs/2408.13430 (2024) - [i119]Eshwar Ram Arunachaleswaran, Natalie Collina, Sampath Kannan, Aaron Roth, Juba Ziani:
Algorithmic Collusion Without Threats. CoRR abs/2409.03956 (2024) - [i118]Natalie Collina, Rabanus Derr, Aaron Roth:
The Value of Ambiguous Commitments in Multi-Follower Games. CoRR abs/2409.05608 (2024) - [i117]Rongting Zhang, Martín Bertrán, Aaron Roth:
Order of Magnitude Speedups for LLM Membership Inference. CoRR abs/2409.14513 (2024) - [i116]Tobias Leemann, Periklis Petridis, Giuseppe Vietri, Dionysis Manousakas, Aaron Roth, Sergül Aydöre:
Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval Augmented Generation. CoRR abs/2410.03461 (2024) - 2023
- [c108]Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth:
Multicalibrated Regression for Downstream Fairness. AIES 2023: 259-286 - [c107]Aaron Roth, Alexander Tolbert, Scott Weinstein:
Reconciling Individual Probability Forecasts✱. FAccT 2023: 101-110 - [c106]Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Batch Multivalid Conformal Prediction. ICLR 2023 - [c105]Yahav Bechavod, Aaron Roth:
Individually Fair Learning with One-Sided Feedback. ICML 2023: 1954-1977 - [c104]Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. ICML 2023: 11459-11492 - [c103]Georgy Noarov, Aaron Roth:
The Statistical Scope of Multicalibration. ICML 2023: 26283-26310 - [c102]Martín Bertrán, Shuai Tang, Aaron Roth, Michael Kearns, Jamie Morgenstern, Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. NeurIPS 2023 - [c101]Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Wealth Dynamics Over Generations: Analysis and Interventions. SaTML 2023: 42-57 - [i115]Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell:
Multicalibration as Boosting for Regression. CoRR abs/2301.13767 (2023) - [i114]Georgy Noarov, Aaron Roth:
The Scope of Multicalibration: Characterizing Multicalibration via Property Elicitation. CoRR abs/2302.08507 (2023) - [i113]Shuai Tang, Sergül Aydöre, Michael Kearns, Saeyoung Rho, Aaron Roth, Yichen Wang, Yu-Xiang Wang, Zhiwei Steven Wu:
Improved Differentially Private Regression via Gradient Boosting. CoRR abs/2303.03451 (2023) - [i112]Siqi Deng, Emily Diana, Michael Kearns, Aaron Roth:
Balanced Filtering via Non-Disclosive Proxies. CoRR abs/2306.15083 (2023) - [i111]Martín Bertrán, Shuai Tang, Michael Kearns, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Scalable Membership Inference Attacks via Quantile Regression. CoRR abs/2307.03694 (2023) - [i110]Sumegha Garg, Christopher Jung, Omer Reingold, Aaron Roth:
Oracle Efficient Online Multicalibration and Omniprediction. CoRR abs/2307.08999 (2023) - [i109]Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Oracle Efficient Algorithms for Groupwise Regret. CoRR abs/2310.04652 (2023) - [i108]Georgy Noarov, Ramya Ramalingam, Aaron Roth, Stephan Xie:
High-Dimensional Prediction for Sequential Decision Making. CoRR abs/2310.17651 (2023) - [i107]Natalie Collina, Aaron Roth, Han Shao:
Efficient Prior-Free Mechanisms for No-Regret Agents. CoRR abs/2311.07754 (2023) - [i106]Shuai Tang, Zhiwei Steven Wu, Sergül Aydöre, Michael Kearns, Aaron Roth:
Membership Inference Attacks on Diffusion Models via Quantile Regression. CoRR abs/2312.05140 (2023) - 2022
- [j32]Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Pipeline Interventions. Math. Oper. Res. 47(4): 3207-3238 (2022) - [j31]Matthew Joseph, Jieming Mao, Aaron Roth:
Exponential Separations in Local Privacy. ACM Trans. Algorithms 18(4): 32:1-32:17 (2022) - [c100]Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto:
Mixed Differential Privacy in Computer Vision. CVPR 2022: 8366-8376 - [c99]Mingzi Niu, Sampath Kannan, Aaron Roth, Rakesh Vohra:
Best vs. All: Equity and Accuracy of Standardized Test Score Reporting. FAccT 2022: 574-586 - [c98]Ira Globus-Harris, Michael Kearns, Aaron Roth:
An Algorithmic Framework for Bias Bounties. FAccT 2022: 1106-1124 - [c97]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi:
Multiaccurate Proxies for Downstream Fairness. FAccT 2022: 1207-1239 - [c96]Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, Aaron Roth:
Online Multivalid Learning: Means, Moments, and Prediction Intervals. ITCS 2022: 82:1-82:24 - [c95]Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Practical Adversarial Multivalid Conformal Prediction. NeurIPS 2022 - [c94]Daniel Lee, Georgy Noarov, Mallesh M. Pai, Aaron Roth:
Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications. NeurIPS 2022 - [c93]Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu:
Private Synthetic Data for Multitask Learning and Marginal Queries. NeurIPS 2022 - [i105]Ira Globus-Harris, Michael Kearns, Aaron Roth:
Beyond the Frontier: Fairness Without Accuracy Loss. CoRR abs/2201.10408 (2022) - [i104]Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang, Aaron Roth, Michael Kearns, Stefano Soatto:
Mixed Differential Privacy in Computer Vision. CoRR abs/2203.11481 (2022) - [i103]Osbert Bastani, Varun Gupta, Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Practical Adversarial Multivalid Conformal Prediction. CoRR abs/2206.01067 (2022) - [i102]Yahav Bechavod, Aaron Roth:
Individually Fair Learning with One-Sided Feedback. CoRR abs/2206.04475 (2022) - [i101]Aaron Roth, Alexander Tolbert, Scott Weinstein:
Reconciling Individual Probability Forecasts. CoRR abs/2209.01687 (2022) - [i100]Ira Globus-Harris, Varun Gupta, Christopher Jung, Michael Kearns, Jamie Morgenstern, Aaron Roth:
Multicalibrated Regression for Downstream Fairness. CoRR abs/2209.07312 (2022) - [i99]Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Wealth Dynamics Over Generations: Analysis and Interventions. CoRR abs/2209.07375 (2022) - [i98]Giuseppe Vietri, Cédric Archambeau, Sergül Aydöre, William Brown, Michael Kearns, Aaron Roth, Amaresh Ankit Siva, Shuai Tang, Zhiwei Steven Wu:
Private Synthetic Data for Multitask Learning and Marginal Queries. CoRR abs/2209.07400 (2022) - [i97]Christopher Jung, Georgy Noarov, Ramya Ramalingam, Aaron Roth:
Batch Multivalid Conformal Prediction. CoRR abs/2209.15145 (2022) - [i96]Travis Dick, Cynthia Dwork, Michael Kearns, Terrance Liu, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics. CoRR abs/2211.03128 (2022) - 2021
- [c92]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth:
Minimax Group Fairness: Algorithms and Experiments. AIES 2021: 66-76 - [c91]Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi:
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. ALT 2021: 931-962 - [c90]Christopher Jung, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra:
Moment Multicalibration for Uncertainty Estimation. COLT 2021: 2634-2678 - [c89]Aaron Roth:
A User Friendly Power Tool for Deriving Online Learning Algorithms (Invited Talk). ESA 2021: 2:1-2:1 - [c88]Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu:
An Algorithmic Framework for Fairness Elicitation. FORC 2021: 2:1-2:19 - [c87]Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi:
Lexicographically Fair Learning: Algorithms and Generalization. FORC 2021: 6:1-6:23 - [c86]Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva:
Differentially Private Query Release Through Adaptive Projection. ICML 2021: 457-467 - [c85]Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Pipeline Interventions. ITCS 2021: 8:1-8:20 - [c84]Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites:
Adaptive Machine Unlearning. NeurIPS 2021: 16319-16330 - [c83]Emily Diana, Travis Dick, Hadi Elzayn, Michael Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani:
Algorithms and Learning for Fair Portfolio Design. EC 2021: 371-389 - [c82]Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld:
A new analysis of differential privacy's generalization guarantees (invited paper). STOC 2021: 9 - [i95]Varun Gupta, Christopher Jung, Georgy Noarov, Mallesh M. Pai, Aaron Roth:
Online Multivalid Learning: Means, Moments, and Prediction Intervals. CoRR abs/2101.01739 (2021) - [i94]Sampath Kannan, Mingzi Niu, Aaron Roth, Rakesh Vohra:
Best vs. All: Equity and Accuracy of Standardized Test Score Reporting. CoRR abs/2102.07809 (2021) - [i93]Emily Diana, Wesley Gill, Ira Globus-Harris, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi:
Lexicographically Fair Learning: Algorithms and Generalization. CoRR abs/2102.08454 (2021) - [i92]Sergül Aydöre, William Brown, Michael Kearns, Krishnaram Kenthapadi, Luca Melis, Aaron Roth, Amaresh Ankit Siva:
Differentially Private Query Release Through Adaptive Projection. CoRR abs/2103.06641 (2021) - [i91]Jinshuo Dong, Aaron Roth, Weijie J. Su:
Rejoinder: Gaussian Differential Privacy. CoRR abs/2104.01987 (2021) - [i90]Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Chris Waites:
Adaptive Machine Unlearning. CoRR abs/2106.04378 (2021) - [i89]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth, Saeed Sharifi-Malvajerdi:
Multiaccurate Proxies for Downstream Fairness. CoRR abs/2107.04423 (2021) - [i88]Georgy Noarov, Mallesh M. Pai, Aaron Roth:
Online Multiobjective Minimax Optimization and Applications. CoRR abs/2108.03837 (2021) - 2020
- [j30]Alexandra Chouldechova, Aaron Roth:
A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63(5): 82-89 (2020) - [j29]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. J. Priv. Confidentiality 10(1) (2020) - [j28]Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth:
Testing differential privacy with dual interpreters. Proc. ACM Program. Lang. 4(OOPSLA): 165:1-165:26 (2020) - [j27]Michael Kearns, Aaron Roth:
Ethical algorithm design. SIGecom Exch. 18(1): 31-36 (2020) - [j26]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. ACM Trans. Economics and Comput. 8(1): 6:1-6:35 (2020) - [c81]Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth:
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. AISTATS 2020: 2830-2840 - [c80]Emily Diana, Michael Kearns, Seth Neel, Aaron Roth:
Optimal, truthful, and private securities lending. ICAIF 2020: 48:1-48:8 - [c79]Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Oracle Efficient Private Non-Convex Optimization. ICML 2020: 7243-7252 - [c78]Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld:
A New Analysis of Differential Privacy's Generalization Guarantees. ITCS 2020: 31:1-31:17 - [c77]Emily Diana, Hadi Elzayn, Michael Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani:
Differentially Private Call Auctions and Market Impact. EC 2020: 541-583 - [c76]Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra:
Fair Prediction with Endogenous Behavior. EC 2020: 677-678 - [c75]Matthew Joseph, Jieming Mao, Aaron Roth:
Exponential Separations in Local Differential Privacy. SODA 2020: 515-527 - [e1]Aaron Roth:
1st Symposium on Foundations of Responsible Computing, FORC 2020, June 1-3, 2020, Harvard University, Cambridge, MA, USA (virtual conference). LIPIcs 156, Schloss Dagstuhl - Leibniz-Zentrum für Informatik 2020, ISBN 978-3-95977-142-9 [contents] - [i87]Daniel Kifer, Solomon Messing, Aaron Roth, Abhradeep Thakurta, Danfeng Zhang:
Guidelines for Implementing and Auditing Differentially Private Systems. CoRR abs/2002.04049 (2020) - [i86]Emily Diana, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi, Juba Ziani:
Differentially Private Call Auctions and Market Impact. CoRR abs/2002.05699 (2020) - [i85]Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth, Juba Ziani:
Pipeline Interventions. CoRR abs/2002.06592 (2020) - [i84]Christopher Jung, Sampath Kannan, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra:
Fair Prediction with Endogenous Behavior. CoRR abs/2002.07147 (2020) - [i83]Emily Diana, Travis Dick, Hadi Elzayn, Michael J. Kearns, Aaron Roth, Zachary Schutzman, Saeed Sharifi-Malvajerdi, Juba Ziani:
Algorithms and Learning for Fair Portfolio Design. CoRR abs/2006.07281 (2020) - [i82]Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi:
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning. CoRR abs/2007.02923 (2020) - [i81]Christopher Jung, Changhwa Lee, Mallesh M. Pai, Aaron Roth, Rakesh Vohra:
Moment Multicalibration for Uncertainty Estimation. CoRR abs/2008.08037 (2020) - [i80]Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth:
Testing Differential Privacy with Dual Interpreters. CoRR abs/2010.04126 (2020) - [i79]Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, Aaron Roth:
Convergent Algorithms for (Relaxed) Minimax Fairness. CoRR abs/2011.03108 (2020)
2010 – 2019
- 2019
- [j25]Gilles Barthe, Christos Dimitrakakis, Marco Gaboardi, Andreas Haeberlen, Aaron Roth, Aleksandra B. Slavkovic:
Program for TPDP 2016. J. Priv. Confidentiality 9(1) (2019) - [j24]Zhiwei Steven Wu, Aaron Roth, Katrina Ligett, Bo Waggoner, Seth Neel:
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. J. Priv. Confidentiality 9(2) (2019) - [j23]Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth:
Fuzzi: a three-level logic for differential privacy. Proc. ACM Program. Lang. 3(ICFP): 93:1-93:28 (2019) - [c74]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
An Empirical Study of Rich Subgroup Fairness for Machine Learning. FAT 2019: 100-109 - [c73]Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman:
Fair Algorithms for Learning in Allocation Problems. FAT 2019: 170-179 - [c72]Sampath Kannan, Aaron Roth, Juba Ziani:
Downstream Effects of Affirmative Action. FAT 2019: 240-248 - [c71]Seth Neel, Aaron Roth, Zhiwei Steven Wu:
How to Use Heuristics for Differential Privacy. FOCS 2019: 72-93 - [c70]Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth:
The Role of Interactivity in Local Differential Privacy. FOCS 2019: 94-105 - [c69]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. ICML 2019: 3000-3008 - [c68]Saeed Sharifi-Malvajerdi, Michael J. Kearns, Aaron Roth:
Average Individual Fairness: Algorithms, Generalization and Experiments. NeurIPS 2019: 8240-8249 - [c67]Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Equal Opportunity in Online Classification with Partial Feedback. NeurIPS 2019: 8972-8982 - [i78]Yahav Bechavod, Katrina Ligett, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Equal Opportunity in Online Classification with Partial Feedback. CoRR abs/1902.02242 (2019) - [i77]Matthew Joseph, Jieming Mao, Seth Neel, Aaron Roth:
The Role of Interactivity in Local Differential Privacy. CoRR abs/1904.03564 (2019) - [i76]Jinshuo Dong, Aaron Roth, Weijie J. Su:
Gaussian Differential Privacy. CoRR abs/1905.02383 (2019) - [i75]Michael J. Kearns, Aaron Roth, Saeed Sharifi-Malvajerdi:
Average Individual Fairness: Algorithms, Generalization and Experiments. CoRR abs/1905.10607 (2019) - [i74]Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, Zhiwei Steven Wu:
Eliciting and Enforcing Subjective Individual Fairness. CoRR abs/1905.10660 (2019) - [i73]Hengchu Zhang, Edo Roth, Andreas Haeberlen, Benjamin C. Pierce, Aaron Roth:
Fuzzi: A Three-Level Logic for Differential Privacy. CoRR abs/1905.12594 (2019) - [i72]Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth:
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. CoRR abs/1906.09231 (2019) - [i71]Matthew Joseph, Jieming Mao, Aaron Roth:
Exponential Separations in Local Differential Privacy Through Communication Complexity. CoRR abs/1907.00813 (2019) - [i70]Seth Neel, Aaron Roth, Giuseppe Vietri, Zhiwei Steven Wu:
Differentially Private Objective Perturbation: Beyond Smoothness and Convexity. CoRR abs/1909.01783 (2019) - [i69]Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, Moshe Shenfeld:
A New Analysis of Differential Privacy's Generalization Guarantees. CoRR abs/1909.03577 (2019) - [i68]Emily Diana, Michael J. Kearns, Seth Neel, Aaron Roth:
Optimal, Truthful, and Private Securities Lending. CoRR abs/1912.06202 (2019) - 2018
- [j22]Sampath Kannan, Jamie Morgenstern, Ryan Rogers, Aaron Roth:
Private Pareto Optimal Exchange. ACM Trans. Economics and Comput. 6(3-4): 12:1-12:25 (2018) - [c66]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Meritocratic Fairness for Infinite and Contextual Bandits. AIES 2018: 158-163 - [c65]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. ICML 2018: 2569-2577 - [c64]Seth Neel, Aaron Roth:
Mitigating Bias in Adaptive Data Gathering via Differential Privacy. ICML 2018: 3717-3726 - [c63]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. NeurIPS 2018: 2231-2241 - [c62]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. NeurIPS 2018: 2381-2390 - [c61]Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth:
Online Learning with an Unknown Fairness Metric. NeurIPS 2018: 2605-2614 - [c60]Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu:
Strategic Classification from Revealed Preferences. EC 2018: 55-70 - [i67]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem. CoRR abs/1801.03423 (2018) - [i66]Stephen Gillen, Christopher Jung, Michael J. Kearns, Aaron Roth:
Online Learning with an Unknown Fairness Metric. CoRR abs/1802.06936 (2018) - [i65]Matthew Joseph, Aaron Roth, Jonathan R. Ullman, Bo Waggoner:
Local Differential Privacy for Evolving Data. CoRR abs/1802.07128 (2018) - [i64]Seth Neel, Aaron Roth:
Mitigating Bias in Adaptive Data Gathering via Differential Privacy. CoRR abs/1806.02329 (2018) - [i63]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
An Empirical Study of Rich Subgroup Fairness for Machine Learning. CoRR abs/1808.08166 (2018) - [i62]Sampath Kannan, Aaron Roth, Juba Ziani:
Downstream Effects of Affirmative Action. CoRR abs/1808.09004 (2018) - [i61]Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Zachary Schutzman:
Fair Algorithms for Learning in Allocation Problems. CoRR abs/1808.10549 (2018) - [i60]Alexandra Chouldechova, Aaron Roth:
The Frontiers of Fairness in Machine Learning. CoRR abs/1810.08810 (2018) - [i59]Seth Neel, Aaron Roth, Zhiwei Steven Wu:
How to Use Heuristics for Differential Privacy. CoRR abs/1811.07765 (2018) - [i58]Matthew Jagielski, Michael J. Kearns, Jieming Mao, Alina Oprea, Aaron Roth, Saeed Sharifi-Malvajerdi, Jonathan R. Ullman:
Differentially Private Fair Learning. CoRR abs/1812.02696 (2018) - 2017
- [j21]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Guilt-free data reuse. Commun. ACM 60(4): 86-93 (2017) - [j20]Aaron Roth:
Pricing information (and its implications): technical perspective. Commun. ACM 60(12): 78 (2017) - [j19]Daniel Winograd-Cort, Andreas Haeberlen, Aaron Roth, Benjamin C. Pierce:
A framework for adaptive differential privacy. Proc. ACM Program. Lang. 1(ICFP): 10:1-10:29 (2017) - [j18]Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman:
An Antifolk Theorem for Large Repeated Games. ACM Trans. Economics and Comput. 5(2): 10:1-10:20 (2017) - [c59]Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Reinforcement Learning. ICML 2017: 1617-1626 - [c58]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Meritocratic Fairness for Cross-Population Selection. ICML 2017: 1828-1836 - [c57]Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM. NIPS 2017: 2566-2576 - [c56]Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu:
Fairness Incentives for Myopic Agents. EC 2017: 369-386 - [c55]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. EC 2017: 519-536 - [i57]Sampath Kannan, Michael J. Kearns, Jamie Morgenstern, Mallesh M. Pai, Aaron Roth, Rakesh V. Vohra, Zhiwei Steven Wu:
Fairness Incentives for Myopic Agents. CoRR abs/1705.02321 (2017) - [i56]Katrina Ligett, Seth Neel, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu:
Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM. CoRR abs/1705.10829 (2017) - [i55]Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
A Convex Framework for Fair Regression. CoRR abs/1706.02409 (2017) - [i54]Jinshuo Dong, Aaron Roth, Zachary Schutzman, Bo Waggoner, Zhiwei Steven Wu:
Strategic Classification from Revealed Preferences. CoRR abs/1710.07887 (2017) - [i53]Michael J. Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu:
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. CoRR abs/1711.05144 (2017) - 2016
- [j17]Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu:
Dual Query: Practical Private Query Release for High Dimensional Data. J. Priv. Confidentiality 7(2) (2016) - [j16]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev:
Private algorithms for the protected in social network search. Proc. Natl. Acad. Sci. USA 113(4): 913-918 (2016) - [j15]Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu:
Private Matchings and Allocations. SIAM J. Comput. 45(6): 1953-1984 (2016) - [j14]Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra:
Do prices coordinate markets? SIGecom Exch. 15(1): 84-88 (2016) - [j13]Paul W. Goldberg, Aaron Roth:
Bounds for the Query Complexity of Approximate Equilibria. ACM Trans. Economics and Comput. 4(4): 24:1-24:25 (2016) - [c54]Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu:
Adaptive Learning with Robust Generalization Guarantees. COLT 2016: 772-814 - [c53]Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar:
Max-Information, Differential Privacy, and Post-selection Hypothesis Testing. FOCS 2016: 487-494 - [c52]Hoda Heidari, Michael J. Kearns, Aaron Roth:
Tight Policy Regret Bounds for Improving and Decaying Bandits. IJCAI 2016: 1562-1570 - [c51]Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu:
Coordination Complexity: Small Information Coordinating Large Populations. ITCS 2016: 281-290 - [c50]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. NIPS 2016: 325-333 - [c49]Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu:
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. NIPS 2016: 1570-1578 - [c48]Ryan M. Rogers, Salil P. Vadhan, Aaron Roth, Jonathan R. Ullman:
Privacy Odometers and Filters: Pay-as-you-Go Composition. NIPS 2016: 1921-1929 - [c47]Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth:
The Strange Case of Privacy in Equilibrium Models. EC 2016: 659 - [c46]Justin Hsu, Zhiyi Huang, Aaron Roth, Zhiwei Steven Wu:
Jointly Private Convex Programming. SODA 2016: 580-599 - [c45]Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra:
Do prices coordinate markets? STOC 2016: 440-453 - [c44]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and learn: optimizing from revealed preferences feedback. STOC 2016: 949-962 - [c43]Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub:
Computer-Aided Verification for Mechanism Design. WINE 2016: 279-293 - [i52]Rachel Cummings, Katrina Ligett, Kobbi Nissim, Aaron Roth, Zhiwei Steven Wu:
Adaptive Learning with Robust Generalization Guarantees. CoRR abs/1602.07726 (2016) - [i51]Ryan M. Rogers, Aaron Roth, Adam D. Smith, Om Thakkar:
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing. CoRR abs/1604.03924 (2016) - [i50]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fairness in Learning: Classic and Contextual Bandits. CoRR abs/1605.07139 (2016) - [i49]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Salil P. Vadhan:
Privacy Odometers and Filters: Pay-as-you-Go Composition. CoRR abs/1605.08294 (2016) - [i48]Aaron Roth, Aleksandrs Slivkins, Jonathan R. Ullman, Zhiwei Steven Wu:
Multidimensional Dynamic Pricing for Welfare Maximization. CoRR abs/1607.05397 (2016) - [i47]Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth:
Rawlsian Fairness for Machine Learning. CoRR abs/1610.09559 (2016) - [i46]Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Aaron Roth:
Fair Learning in Markovian Environments. CoRR abs/1611.03071 (2016) - 2015
- [j12]Moshe Babaioff, Liad Blumrosen, Aaron Roth:
Auctions with online supply. Games Econ. Behav. 90: 227-246 (2015) - [j11]Arpita Ghosh, Aaron Roth:
Selling privacy at auction. Games Econ. Behav. 91: 334-346 (2015) - [j10]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and learn: optimizing from revealed preferences feedback. SIGecom Exch. 14(1): 101-104 (2015) - [c42]Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth:
Online Learning and Profit Maximization from Revealed Preferences. AAAI 2015: 770-776 - [c41]Rachel Cummings, Katrina Ligett, Aaron Roth, Zhiwei Steven Wu, Juba Ziani:
Accuracy for Sale: Aggregating Data with a Variance Constraint. ITCS 2015: 317-324 - [c40]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Generalization in Adaptive Data Analysis and Holdout Reuse. NIPS 2015: 2350-2358 - [c39]Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub:
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy. POPL 2015: 55-68 - [c38]Sampath Kannan, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth:
Private Pareto Optimal Exchange. EC 2015: 261-278 - [c37]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Inducing Approximately Optimal Flow Using Truthful Mediators. EC 2015: 471-488 - [c36]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). SODA 2015: 1890-1903 - [c35]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Leon Roth:
Preserving Statistical Validity in Adaptive Data Analysis. STOC 2015: 117-126 - [c34]Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Privacy and Truthful Equilibrium Selection for Aggregative Games. WINE 2015: 286-299 - [i45]Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Inducing Approximately Optimal Flow Using Truthful Mediators. CoRR abs/1502.04019 (2015) - [i44]Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub:
Computer-aided verification in mechanism design. CoRR abs/1502.04052 (2015) - [i43]Aaron Roth, Jonathan R. Ullman, Zhiwei Steven Wu:
Watch and Learn: Optimizing from Revealed Preferences Feedback. CoRR abs/1504.01033 (2015) - [i42]Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev:
Privacy for the Protected (Only). CoRR abs/1506.00242 (2015) - [i41]Shahin Jabbari, Ryan M. Rogers, Aaron Roth, Zhiwei Steven Wu:
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs. CoRR abs/1506.02162 (2015) - [i40]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Generalization in Adaptive Data Analysis and Holdout Reuse. CoRR abs/1506.02629 (2015) - [i39]Rachel Cummings, Katrina Ligett, Mallesh M. Pai, Aaron Roth:
The Strange Case of Privacy in Equilibrium Models. CoRR abs/1508.03080 (2015) - [i38]Rachel Cummings, Katrina Ligett, Jaikumar Radhakrishnan, Aaron Roth, Zhiwei Steven Wu:
Coordination Complexity: Small Information Coordinating Large Populations. CoRR abs/1508.03735 (2015) - [i37]Justin Hsu, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth, Rakesh Vohra:
Do Prices Coordinate Markets? CoRR abs/1511.00925 (2015) - [i36]Michael J. Kearns, Mallesh M. Pai, Ryan M. Rogers, Aaron Roth, Jonathan R. Ullman:
Robust Mediators in Large Games. CoRR abs/1512.02698 (2015) - 2014
- [j9]Cynthia Dwork, Aaron Roth:
The Algorithmic Foundations of Differential Privacy. Found. Trends Theor. Comput. Sci. 9(3-4): 211-407 (2014) - [j8]Aaron Roth:
Differential Privacy as a Tool for Mechanism Design in Large Systems. SIGMETRICS Perform. Evaluation Rev. 42(3): 39 (2014) - [c33]Justin Hsu, Marco Gaboardi, Andreas Haeberlen, Sanjeev Khanna, Arjun Narayan, Benjamin C. Pierce, Aaron Roth:
Differential Privacy: An Economic Method for Choosing Epsilon. CSF 2014: 398-410 - [c32]Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan R. Ullman:
Privately Solving Linear Programs. ICALP (1) 2014: 612-624 - [c31]Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu:
Dual Query: Practical Private Query Release for High Dimensional Data. ICML 2014: 1170-1178 - [c30]Michael J. Kearns, Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman:
Mechanism design in large games: incentives and privacy. ITCS 2014: 403-410 - [c29]Paul W. Goldberg, Aaron Roth:
Bounds for the query complexity of approximate equilibria. EC 2014: 639-656 - [c28]Ryan M. Rogers, Aaron Roth:
Asymptotically truthful equilibrium selection in large congestion games. EC 2014: 771-782 - [c27]Arpita Ghosh, Katrina Ligett, Aaron Roth, Grant Schoenebeck:
Buying private data without verification. EC 2014: 931-948 - [c26]Zhiyi Huang, Aaron Roth:
Exploiting Metric Structure for Efficient Private Query Release. SODA 2014: 523-534 - [c25]Shaddin Dughmi, Nicole Immorlica, Aaron Roth:
Constrained Signaling in Auction Design. SODA 2014: 1341-1357 - [c24]Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu:
Private matchings and allocations. STOC 2014: 21-30 - [i35]Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Zhiwei Steven Wu:
Dual Query: Practical Private Query Release for High Dimensional Data. CoRR abs/1402.1526 (2014) - [i34]Mallesh M. Pai, Aaron Roth, Jonathan R. Ullman:
An Anti-Folk Theorem for Large Repeated Games with Imperfect Monitoring. CoRR abs/1402.2801 (2014) - [i33]Justin Hsu, Marco Gaboardi, Andreas Haeberlen, Sanjeev Khanna, Arjun Narayan, Benjamin C. Pierce, Aaron Roth:
Differential Privacy: An Economic Method for Choosing Epsilon. CoRR abs/1402.3329 (2014) - [i32]Justin Hsu, Aaron Roth, Tim Roughgarden, Jonathan R. Ullman:
Privately Solving Linear Programs. CoRR abs/1402.3631 (2014) - [i31]Arpita Ghosh, Katrina Ligett, Aaron Roth, Grant Schoenebeck:
Buying Private Data without Verification. CoRR abs/1404.6003 (2014) - [i30]Sampath Kannan, Jamie Morgenstern, Aaron Roth, Zhiwei Steven Wu:
Approximately Stable, School Optimal, and Student-Truthful Many-to-One Matchings (via Differential Privacy). CoRR abs/1407.2640 (2014) - [i29]Sampath Kannan, Jamie Morgenstern, Ryan M. Rogers, Aaron Roth:
Private Pareto Optimal Exchange. CoRR abs/1407.2641 (2014) - [i28]Gilles Barthe, Marco Gaboardi, Emilio Jesús Gallego Arias, Justin Hsu, Aaron Roth, Pierre-Yves Strub:
Higher-Order Approximate Relational Refinement Types for Mechanism Design and Differential Privacy. CoRR abs/1407.6845 (2014) - [i27]Kareem Amin, Rachel Cummings, Lili Dworkin, Michael J. Kearns, Aaron Roth:
Online Learning and Profit Maximization from Revealed Preferences. CoRR abs/1407.7294 (2014) - [i26]Rachel Cummings, Michael J. Kearns, Aaron Roth, Zhiwei Steven Wu:
Privacy and Truthful Equilibrium Selection for Aggregative Games. CoRR abs/1407.7740 (2014) - [i25]Justin Hsu, Zhiyi Huang, Aaron Roth, Zhiwei Steven Wu:
Jointly Private Convex Programming. CoRR abs/1411.0998 (2014) - [i24]Cynthia Dwork, Vitaly Feldman, Moritz Hardt, Toniann Pitassi, Omer Reingold, Aaron Roth:
Preserving Statistical Validity in Adaptive Data Analysis. CoRR abs/1411.2664 (2014) - 2013
- [j7]Aaron Roth:
Coordination When Information is Scarce: How privacy can help. XRDS 20(1): 14-16 (2013) - [j6]Avrim Blum, Katrina Ligett, Aaron Roth:
A learning theory approach to noninteractive database privacy. J. ACM 60(2): 12:1-12:25 (2013) - [j5]Anupam Gupta, Moritz Hardt, Aaron Roth, Jonathan R. Ullman:
Privately Releasing Conjunctions and the Statistical Query Barrier. SIAM J. Comput. 42(4): 1494-1520 (2013) - [j4]Mallesh M. Pai, Aaron Roth:
Privacy and mechanism design. SIGecom Exch. 12(1): 8-29 (2013) - [j3]Shaddin Dughmi, Nicole Immorlica, Aaron Roth:
Constrained signaling for welfare and revenue maximization. SIGecom Exch. 12(1): 53-56 (2013) - [c23]Aaron Roth:
Differential privacy, equilibrium, and efficient allocation of resources. Allerton 2013: 1593-1597 - [c22]Avrim Blum, Aaron Roth:
Fast Private Data Release Algorithms for Sparse Queries. APPROX-RANDOM 2013: 395-410 - [c21]Moritz Hardt, Aaron Roth:
Beyond worst-case analysis in private singular vector computation. STOC 2013: 331-340 - [c20]Justin Hsu, Aaron Roth, Jonathan R. Ullman:
Differential privacy for the analyst via private equilibrium computation. STOC 2013: 341-350 - [i23]Shaddin Dughmi, Nicole Immorlica, Aaron Roth:
Constrained Signaling for Welfare and Revenue Maximization. CoRR abs/1302.4713 (2013) - [i22]Mallesh M. Pai, Aaron Roth:
Privacy and Mechanism Design. CoRR abs/1306.2083 (2013) - [i21]Justin Hsu, Zhiyi Huang, Aaron Roth, Tim Roughgarden, Zhiwei Steven Wu:
Private Matchings and Allocations. CoRR abs/1311.2828 (2013) - [i20]Paul W. Goldberg, Aaron Roth:
Bounds for the Query Complexity of Approximate Equilibria. Electron. Colloquium Comput. Complex. TR13 (2013) - 2012
- [j2]Christine Chung, Katrina Ligett, Kirk Pruhs, Aaron Roth:
The Power of Fair Pricing Mechanisms. Algorithmica 63(3): 634-644 (2012) - [j1]Aaron Roth:
Buying private data at auction: the sensitive surveyor's problem. SIGecom Exch. 11(1): 1-8 (2012) - [c19]Justin Hsu, Sanjeev Khanna, Aaron Roth:
Distributed Private Heavy Hitters. ICALP (1) 2012: 461-472 - [c18]Aaron Roth, Grant Schoenebeck:
Conducting truthful surveys, cheaply. EC 2012: 826-843 - [c17]Moritz Hardt, Aaron Roth:
Beating randomized response on incoherent matrices. STOC 2012: 1255-1268 - [c16]Anupam Gupta, Aaron Roth, Jonathan R. Ullman:
Iterative Constructions and Private Data Release. TCC 2012: 339-356 - [c15]Morteza Zadimoghaddam, Aaron Roth:
Efficiently Learning from Revealed Preference. WINE 2012: 114-127 - [c14]Katrina Ligett, Aaron Roth:
Take It or Leave It: Running a Survey When Privacy Comes at a Cost. WINE 2012: 378-391 - [i19]Katrina Ligett, Aaron Roth:
Take it or Leave it: Running a Survey when Privacy Comes at a Cost. CoRR abs/1202.4741 (2012) - [i18]Justin Hsu, Sanjeev Khanna, Aaron Roth:
Distributed Private Heavy Hitters. CoRR abs/1202.4910 (2012) - [i17]Aaron Roth, Grant Schoenebeck:
Conducting Truthful Surveys, Cheaply. CoRR abs/1203.0353 (2012) - [i16]Justin Hsu, Aaron Roth, Jonathan R. Ullman:
Differential Privacy for the Analyst via Private Equilibrium Computation. CoRR abs/1211.0877 (2012) - [i15]Moritz Hardt, Aaron Roth:
Beyond Worst-Case Analysis in Private Singular Vector Computation. CoRR abs/1211.0975 (2012) - [i14]Morteza Zadimoghaddam, Aaron Roth:
Efficiently Learning from Revealed Preference. CoRR abs/1211.4150 (2012) - [i13]Zhiyi Huang, Aaron Roth:
Exploiting Metric Structure for Efficient Private Query Release. CoRR abs/1211.7302 (2012) - 2011
- [c13]Arpita Ghosh, Aaron Roth:
Selling privacy at auction. EC 2011: 199-208 - [c12]Anupam Gupta, Moritz Hardt, Aaron Roth, Jonathan R. Ullman:
Privately releasing conjunctions and the statistical query barrier. STOC 2011: 803-812 - [i12]Anupam Gupta, Aaron Roth, Jonathan R. Ullman:
Iterative Constructions and Private Data Release. CoRR abs/1107.3731 (2011) - [i11]Avrim Blum, Katrina Ligett, Aaron Roth:
A Learning Theory Approach to Non-Interactive Database Privacy. CoRR abs/1109.2229 (2011) - [i10]Moritz Hardt, Aaron Roth:
Beating Randomized Response on Incoherent Matrices. CoRR abs/1111.0623 (2011) - [i9]Avrim Blum, Aaron Roth:
Fast Private Data Release Algorithms for Sparse Queries. CoRR abs/1111.6842 (2011) - 2010
- [c11]Aaron Roth:
Differential Privacy and the Fat-Shattering Dimension of Linear Queries. APPROX-RANDOM 2010: 683-695 - [c10]Christine Chung, Katrina Ligett, Kirk Pruhs, Aaron Roth:
The Power of Fair Pricing Mechanisms. LATIN 2010: 554-564 - [c9]Moshe Babaioff, Liad Blumrosen, Aaron Roth:
Auctions with online supply. EC 2010: 13-22 - [c8]Aaron Roth, Maria-Florina Balcan, Adam Kalai, Yishay Mansour:
On the Equilibria of Alternating Move Games. SODA 2010: 805-816 - [c7]Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth, Kunal Talwar:
Differentially Private Combinatorial Optimization. SODA 2010: 1106-1125 - [c6]Aaron Roth, Tim Roughgarden:
Interactive privacy via the median mechanism. STOC 2010: 765-774 - [c5]Anupam Gupta, Aaron Roth, Grant Schoenebeck, Kunal Talwar:
Constrained Non-monotone Submodular Maximization: Offline and Secretary Algorithms. WINE 2010: 246-257 - [i8]Anupam Gupta, Aaron Roth, Grant Schoenebeck, Kunal Talwar:
Constrained Non-Monotone Submodular Maximization: Offline and Secretary Algorithms. CoRR abs/1003.1517 (2010) - [i7]Aaron Roth:
Differential Privacy and the Fat-Shattering Dimension of Linear Queries. CoRR abs/1004.3205 (2010) - [i6]Anupam Gupta, Moritz Hardt, Aaron Roth, Jonathan R. Ullman:
Privately Releasing Conjunctions and the Statistical Query Barrier. CoRR abs/1011.1296 (2010) - [i5]Arpita Ghosh, Aaron Roth:
Selling Privacy at Auction. CoRR abs/1011.1375 (2010)
2000 – 2009
- 2009
- [i4]Kunal Talwar, Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth:
Differentially Private Combinatorial Optimization. Parameterized complexity and approximation algorithms 2009 - [i3]Anupam Gupta, Katrina Ligett, Frank McSherry, Aaron Roth, Kunal Talwar:
Differentially Private Approximation Algorithms. CoRR abs/0903.4510 (2009) - [i2]Moshe Babaioff, Liad Blumrosen, Aaron Roth:
Auctions with Online Supply. CoRR abs/0905.3429 (2009) - [i1]Aaron Roth, Tim Roughgarden:
The Median Mechanism: Interactive and Efficient Privacy with Multiple Queries. CoRR abs/0911.1813 (2009) - 2008
- [c4]Christine Chung, Katrina Ligett, Kirk Pruhs, Aaron Roth:
The Price of Stochastic Anarchy. SAGT 2008: 303-314 - [c3]Avrim Blum, MohammadTaghi Hajiaghayi, Katrina Ligett, Aaron Roth:
Regret minimization and the price of total anarchy. STOC 2008: 373-382 - [c2]Avrim Blum, Katrina Ligett, Aaron Roth:
A learning theory approach to non-interactive database privacy. STOC 2008: 609-618 - [c1]Aaron Roth:
The Price of Malice in Linear Congestion Games. WINE 2008: 118-125
Coauthor Index
Michael Kearns
aka: Michael J. Kearns
aka: Michael J. Kearns
[c116] [c115] [c112] [c110] [i128] [i123] [i122] [i121] [c108] [c104] [c102] [i115] [i113] [i112] [i111] [i106] [c100] [c98] [c97] [c93] [i105] [i104] [i100] [i98] [i96] [c92] [c88] [c87] [c86] [c83] [i93] [i92] [i89] [j27] [c80] [c77] [i86] [i83] [i79] [c74] [c73] [c69] [c68] [i75] [i74] [i68] [c66] [c65] [c61] [i66] [i63] [i61] [i58] [c59] [c58] [c56] [i57] [i55] [i53] [j16] [c52] [c50] [i50] [i47] [i46] [c42] [c34] [i42] [i36] [c30] [i27] [i26]
Ryan Rogers 0002
aka: Ryan M. Rogers
aka: Ryan M. Rogers
Rakesh V. Vohra
aka: Rakesh Vohra
aka: Rakesh Vohra
[c112] [c110] [i121] [c102] [i113] [i111] [i106] [c93] [i98] [i96] [c88] [j26] [c79] [j24] [c74] [c71] [c67] [i78] [i74] [i70] [c65] [c63] [c60] [i67] [i63] [i59] [c58] [c57] [c56] [c55] [i57] [i56] [i54] [i53] [j17] [j16] [j15] [c54] [c51] [c49] [c46] [c44] [i52] [i48] [j10] [c41] [c37] [c36] [c34] [i45] [i43] [i42] [i41] [i38] [c31] [c24] [i35] [i30] [i26] [i25] [i21]
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-15 19:33 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
